Reducing atmospheric noise in RST analysis of TIR satellite radiances for earthquakes prone areas satellite monitoring

Lisi, Mariano; Filizzola, Carolina; Genzano, Nicola; Paciello, Rossana; Pergola, Nicola; Tramutoli, Valerio

doi:10.1016/j.pce.2015.07.013

Cited by 24 publications

(17 citation statements)

References 46 publications

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“…Research suggests that the use of satellite-derived land surface temperature (LST) data can provide significant advantages in this field of study [6,31,32]. LST products are estimated from top-of-atmosphere brightness temperatures after cloud-masking and application of atmospheric corrections.…”

Section: Input Datamentioning

confidence: 99%

“…Observations that are referred to as 'thermal anomalies' have been reported to precede earthquakes worldwide since the 1980s (see, for example, [1] and references therein). Several reports describe sudden increases in brightness temperatures (BT) recorded by satellite sensors [2][3][4]; in surface temperatures derived using either satellite observations [5][6][7] or numerical simulations [8,9]; in air temperatures recorded with ground-based meteorological stations [10,11]; in satellite-based outgoing longwave radiation (OLR) [12,13]; in surface latent heat flux (SLHF) [14,15]; and in soil temperatures measured on-site [16,17].…”

Section: Introductionmentioning

confidence: 99%

“…= 0.844, p = 0.475, partial η 2 = 0.037 NONE F(3, 54) = 2.279, p = 0.090, partial η 2 = 0.112 NONE F(3, 67) = 0.008, p = 0.999, partial η 2 < 0.001 NONE F(3, 55) = 0.626, p = 0.601, partial η 971, 196.116) = 11.738, p < 0.001, partial η 2 = 0.151 YES * F(2.277, 122.971) = 4.787, p = 0.007, partial η 2 = 0.081 YES ** F(2.968, 116.355) = 9.927, p < 0.001,partial η 2 = 0.129 NONE F(2.535, 139.413) = 6.631, p < 0.001, partial η 2 = 0.1086 Simple effect of distance NONE F(3, 66) = 0.333, p = 0.801, partial η 2 = 0.015 NONE F(3, 54) = 0.060, p = 0.980, partial η 2 = 0.003 NONE F(3, 67) = 0.084, p = 0.969, partial η 2 = 0.004 NONE F(3, 55) = 0.468, p = 0.706, partial η 2 = 0.025 7 Simple effect of year NONE F(1, 66) = 0.284, p = 0.596, partial η 2 = 0.004 NONE F(1, 54) = 1.967, p = 0.167, partial η 2 = 0.035 NONE F(1, 67) = 1.834, p = 0.180, partial η 2 = 0.027 YES ** F(1, 55) = 14.914, p < 0.001, partial η 2 = 0.213…”

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confidence: 98%

“…LST products are estimated from top-of-atmosphere brightness temperatures after cloud-masking and application of atmospheric corrections. As a result, they are less affected by artefacts of atmospheric origin [6]. Operational LST products of improved accuracy, compared to previous decades [33], are consistently derived at a near-global scale and accompanied by uncertainty estimates [34][35][36][37].…”

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Time Series Analysis of Land Surface Temperatures in 20 Earthquake Cases Worldwide

et al. 2018

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Earthquakes are reported to be preceded by anomalous increases in satellite-recorded thermal emissions, but published results are often contradicting and/or limited to short periods and areas around the earthquake. We apply a methodology that allows to detect subtle, localized spatio-temporal fluctuations in hyper-temporal, geostationary-based land surface temperature (LST) data. We study 10 areas worldwide, covering 20 large (Mw > 5.5) and shallow (<35 km) land-based earthquakes. We compare years and locations with and without earthquake, and we statistically evaluate our findings with respect to distance from epicentra and temporal coincidence with earthquakes. We detect anomalies throughout the duration of all datasets, at various distances from the earthquake, and in years with and without earthquake alike. We find no distinct repeated patterns in the case of earthquakes that happen in the same region in different years. We conclude that earthquakes do not have a significant effect on detected LST anomalies.Remote Sens. 2019, 11, 61 2 of 25 in [25]). Furthermore, anomalies are often found to relate to atmospheric influences and artefacts due to data processing [26].The objective of this study is to make use of the advantages provided by geostationary-based LST data and of a recently published methodological approach, in order to examine the presence of thermal anomalies shortly before large, shallow, land-based earthquakes. We consider that an earthquake is large when it has magnitude larger than Mw 5.5 and that an earthquake is shallow when it has focal depth <35 km. We study 20 earthquake cases in 10 study areas around the world, with different local environmental and climatic conditions. We apply a methodology which suppresses large-scale patterns in the satellite signal time series and isolates only spatially localized fluctuations [27]. This methodology allows to constrain the spatial extent of detected anomalies and the time of their occurrence. Thermal anomalies may appear for a variety of reasons other than earthquakes, including spatiotemporal variations of surface spectral emissivity [28] and local atmospheric conditions, like atmospheric inversions [29]. We therefore test the hypothesis that more anomalies would be detected at closer distances to the earthquake, shortly prior or during the earthquake, and only in years with earthquake occurrence. This hypothesis is supported by published research [30], concluding that anomalies increase with increasing earthquake magnitude; anomalies are found predominantly near the epicenter, one day before and on the day of the earthquake; and anomalies are more easily observed during shallow earthquakes than the deep ones. We statistically evaluate our findings, taking into account the spatial and temporal occurrence of detected anomalies and earthquakes. Materials and Methods Input DataResearch suggests that the use of satellite-derived land surface temperature (LST) data can provide significant advantages in this field of study [6,31,32]. LST products are esti...

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Section: Input Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 98%

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confidence: 99%

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Time Series Analysis of Land Surface Temperatures in 20 Earthquake Cases Worldwide

et al. 2018

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“…However, the earthquake thermal anomalies associated with small magnitude earthquake was also interesting [8]. Many studies have found that the thermal remote sensors can detect the thermal anomalies from seismic activities [3,4,[6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Thermal Anomalies Detection Using Comparative Method for Small Earthquake

Wongpornchai

Suwanprasit

2018

MATEC Web Conf.

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Thermal anomaly is one of the earthquake precursor in the earthquake preparatory phase. Remote sensing in thermal region has been employed based on the concept of stress accumulation in the active plate tectonics region, which may be transformed as temperature variation prior to earthquake. MODIS Land Surface Temperature has been commonly used to locate the thermal anomalies before the earthquake. Recently researches have been focusing on moderate or large magnitude earthquake events. In Thailand, small earthquake can severely damage the unprepared area. This study, the daily day- and nighttime data of MODIS MOD11A1 product for 30 days before and 15 days after the earthquake on April 22, 2007, in Wiang Pa Pao District, Chiang Rai Province, Thailand, were processed and analysed to locate possibility of thermal anomalies. Thermal anomalies before and after the earthquakes were detected using the comparative method. The result found that the thermal anomaly temperature could be high up to 4.1 - 10.9 C which occurred in 21 - 22 days prior to the earthquake. Therefore, it may conclude that small earthquake can also release energy as the detectable thermal anomaly. However, more study about the relationship between thermal precursor and earthquake is needed to continue.

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